{"id":"W65872913","doi":"","title":"Identifying High Collision Locations Without Traffic Volume Data","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Collision; Computer science; Traffic volume; Volume (thermodynamics); Negative binomial distribution; Binomial distribution; Overdispersion; Simulation; Data mining; Transport engineering; Statistics; Engineering; Computer security; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008316629,0.000602296,0.0006823987,0.001579427,0.001566937,0.0002956486,0.001796399,0.0005360598,0.0006301415],"category_scores_gemma":[0.000244934,0.0006328801,0.0001953921,0.003237087,0.0008745794,0.00307519,0.00004753594,0.002606199,0.0009946683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374029,"about_ca_system_score_gemma":0.0004159945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002961039,"about_ca_topic_score_gemma":0.008842607,"domain_scores_codex":[0.988722,0.000877286,0.001561844,0.00119311,0.004753842,0.002891946],"domain_scores_gemma":[0.9940093,0.0008105564,0.0001384322,0.001684554,0.002141603,0.001215567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001583786,0.002382116,0.3977115,0.004023741,0.001130775,0.0002320243,0.05594911,0.3078604,0.00604224,0.0145082,0.1723727,0.0362034],"study_design_scores_gemma":[0.001962572,0.0002218517,0.8911464,0.0004656138,0.0001227461,0.000001915639,0.01473633,0.01360712,0.001076701,0.0001912231,0.07538953,0.001078038],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821339,0.001532312,0.008064836,0.00081547,0.001023533,0.001997887,0.001748736,0.001311634,0.001371726],"genre_scores_gemma":[0.9859461,0.001397172,0.005619866,0.00002891453,0.0007598345,0.0003950231,0.004205108,0.0002460906,0.001401852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4934348,"threshold_uncertainty_score":0.9997832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09699712958836344,"score_gpt":0.3772918317837832,"score_spread":0.2802947021954198,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}